SELDI-TOF-MS is rapidly gaining popularity as a screening tool for clinical applications of proteomics. Application of adequate statistical techniques in all the stages from measurement to information is obligatory. One of the statistical methods often used in proteomics is classification: the assignment of subjects to discrete categories, for example healthy or diseased. Lately, many new classification methods have been developed, often specifically for the analysis of X-omics data. For proteomics studies a good strategy for evaluating classification results is of prime importance, because usually the number of objects will be small and it would be wasteful to set aside part of these as a 'mere' test set. The present paper offers such a strategy in the form of a protocol which can be used for choosing among different statistical classification methods and obtaining figures of merit of their performance. This paper also illustrates the usefulness of proteomics in a clinical setting, serum samples from Gaucher disease patients, when used in combination with an appropriate classification method.

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The potential of proton transfer reaction mass spectrometry (PTR-MS) as a tool for classification of milk fats was evaluated in relation to quality and authentication issues. Butters and butter oils were subjected to heat and off-flavouring treatments in order to create sensorially defective samples. The effect of the treatments was evaluated by means of PTR-MS analysis, sensory analysis and classical chemical analysis. Subsequently, partial least square-discriminant analysis models (PLS-DA) were fitted to predict the matrix (butter/butter oil) and the sensory grades of the samples from their PTR-MS data. Using a 10-fold cross-validation scheme, 84% of the samples were successfully classified into butter and butter oil classes. Regarding sensory quality, 89% of the samples were correctly classified. As the milk fats were fairly successfully classified by the combination of PTR-MS and PLS-DA, this combination seems a promising approach with potential applications in quality control and control of regulations.

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